ABSTRACT Here we propose a series of studies to advance health equity in stroke. This is a collaborative program between Morehouse School of Medicine (MSM) Emory University, Baylor College of Medicine, and the Grady Memorial Hospital. MSM has established an intimate relationship and bond of trust within the minority community thus positioning our research team to reach high risk stroke patients and underserved minorities. We will develop and translate advanced technological approaches to identify which patients should receive thrombolytic therapy, and those patients who are at highest risk of adverse effects. We will utilize novel biostatistics to assess tPA therapy for stroke African American women. MSM has achieved national stature based on its community outreach and innovative, culturally sensitive care delivery programs. Thus the discovery, and establishment of translational implementation of novel solutions to health disparities in high-risk minority communities is our focus. Accordingly, we offer two aims: Aim One- Perform a prospective study of tPA safety and efficacy in African American Female Patients. Retrospective analysis of tPA studies show tPA to have limited benefit in African American Women. If tPA has less beneficial effect, we expect to observe a higher incidence of adverse reporting in this patient population. Here we perform a prospective study to determine efficacy and safety of tPA in African American patients. These data will give importance to the test determined in Aim Two. Aim Two- Investigate transcriptome patterns in response to tPA administration as a predictor of therapeutic response. African American women have a poor response to tPA. To test the utility of RNA-Seq profile to predict response to tPA, blood sample will be obtained from men and women admitted to Grady Memorial Hospital who have suffered stroke. Patients who are administered thrombolytic therapy (tPA) will be stratified and RNA-Seq data subjected to modeling machine learning algorithms, to identify the most accurate predictive model, and compared to traditional predictors of outcome.